| --- |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - generated_from_trainer |
| - dataset_size:315 |
| - loss:CosineSimilarityLoss |
| base_model: google/embeddinggemma-300m |
| widget: |
| - source_sentence: In-House Replenishment Does Not Update Quantities in Product Location |
| History / Batches After Stock Transfer |
| sentences: |
| - Add "Primary Location Quantity" column in Suggestions section of In-House Replenishments |
| UI |
| - Stock and Min On Hand Column Sorting Not Working in Manage Products UI |
| - Previous Surcharge and New Surcharge Displayed as Dollar Amount Instead of Percentage |
| in Product Price History |
| - source_sentence: Quantity field in Cart Items is not manually editable |
| sentences: |
| - Unable to Search and Select Product in Select Product Catalog During In-House |
| Replenishment |
| - Commodity Start At - Commodity End At Filter Not Working in Inventory Master List |
| Report |
| - "Customer Orders UI \x96 Display \"Return\" for Return Clone Orders in Type Column" |
| - source_sentence: Inbound process blocks receiving when Expiration Date or Serial |
| Number is mandatory |
| sentences: |
| - '"Order Received" Button Not Functioning During Product Inbound' |
| - Product stock becomes negative after POS delivery and stock count displayed as |
| -1 |
| - Update Packing Slip date format to MM-DD-YYYY |
| - source_sentence: Cycle Count Variance Report Displays No Data Despite Available |
| Yearly Inventory Count Records |
| sentences: |
| - System allows counted quantity higher than available stock during cycle count |
| - Incorrect page title, wrong PDF icon, and latest variance data not loading in |
| Cycle Count Variance Report |
| - Move "Average Price" Menu From Reports to Warehouse > Manage Inventory |
| - source_sentence: Misaligned Template Section Fields and Inconsistent Invoice Layout |
| Compared to UPS Orders |
| sentences: |
| - SKU Toggle Prints Commodity Code (CC) Instead of SKU in Location Labels |
| - Inventory Master List Displays Active/Inactive Products While Manage Products |
| Uses Different Status Visibility Logic |
| - PDF export button icon appears similar to Excel icon in Inventory Master List |
| report |
| pipeline_tag: sentence-similarity |
| library_name: sentence-transformers |
| metrics: |
| - pearson_cosine |
| - spearman_cosine |
| model-index: |
| - name: SentenceTransformer based on google/embeddinggemma-300m |
| results: |
| - task: |
| type: semantic-similarity |
| name: Semantic Similarity |
| dataset: |
| name: ticket similarity eval |
| type: ticket-similarity-eval |
| metrics: |
| - type: pearson_cosine |
| value: 0.8735255680387198 |
| name: Pearson Cosine |
| - type: spearman_cosine |
| value: 0.819178435361286 |
| name: Spearman Cosine |
| --- |
| |
| # SentenceTransformer based on google/embeddinggemma-300m |
|
|
| This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for retrieval. |
|
|
| ## Model Details |
|
|
| ### Model Description |
| - **Model Type:** Sentence Transformer |
| - **Base model:** [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m) <!-- at revision 57c266a740f537b4dc058e1b0cda161fd15afa75 --> |
| - **Maximum Sequence Length:** 2048 tokens |
| - **Output Dimensionality:** 768 dimensions |
| - **Similarity Function:** Cosine Similarity |
| - **Supported Modality:** Text |
| <!-- - **Training Dataset:** Unknown --> |
| <!-- - **Language:** Unknown --> |
| <!-- - **License:** Unknown --> |
|
|
| ### Model Sources |
|
|
| - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
| - **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers) |
| - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
|
|
| ### Full Model Architecture |
|
|
| ``` |
| SentenceTransformer( |
| (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'Gemma3TextModel'}) |
| (1): Pooling({'embedding_dimension': 768, 'pooling_mode': 'mean', 'include_prompt': True}) |
| (2): Dense({'in_features': 768, 'out_features': 3072, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity', 'module_input_name': 'sentence_embedding', 'module_output_name': 'sentence_embedding'}) |
| (3): Dense({'in_features': 3072, 'out_features': 768, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity', 'module_input_name': 'sentence_embedding', 'module_output_name': 'sentence_embedding'}) |
| (4): Normalize({}) |
| ) |
| ``` |
|
|
| ## Usage |
|
|
| ### Direct Usage (Sentence Transformers) |
|
|
| First install the Sentence Transformers library: |
|
|
| ```bash |
| pip install -U sentence-transformers |
| ``` |
| Then you can load this model and run inference. |
| ```python |
| from sentence_transformers import SentenceTransformer |
| |
| # Download from the 🤗 Hub |
| model = SentenceTransformer("kevin-rice/embeddinggemma-ticket-similarity") |
| # Run inference |
| queries = [ |
| 'Misaligned Template Section Fields and Inconsistent Invoice Layout Compared to UPS Orders', |
| ] |
| documents = [ |
| 'PDF export button icon appears similar to Excel icon in Inventory Master List report', |
| 'Inventory Master List Displays Active/Inactive Products While Manage Products Uses Different Status Visibility Logic', |
| 'SKU Toggle Prints Commodity Code (CC) Instead of SKU in Location Labels', |
| ] |
| query_embeddings = model.encode_query(queries) |
| document_embeddings = model.encode_document(documents) |
| print(query_embeddings.shape, document_embeddings.shape) |
| # [1, 768] [3, 768] |
| |
| # Get the similarity scores for the embeddings |
| similarities = model.similarity(query_embeddings, document_embeddings) |
| print(similarities) |
| # tensor([[0.3035, 0.6568, 0.2639]]) |
| ``` |
| <!-- |
| ### Direct Usage (Transformers) |
|
|
| <details><summary>Click to see the direct usage in Transformers</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Downstream Usage (Sentence Transformers) |
|
|
| You can finetune this model on your own dataset. |
|
|
| <details><summary>Click to expand</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Out-of-Scope Use |
|
|
| *List how the model may foreseeably be misused and address what users ought not to do with the model.* |
| --> |
|
|
| ## Evaluation |
|
|
| ### Metrics |
|
|
| #### Semantic Similarity |
|
|
| * Dataset: `ticket-similarity-eval` |
| * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.sentence_transformer.evaluation.EmbeddingSimilarityEvaluator) |
|
|
| | Metric | Value | |
| |:--------------------|:-----------| |
| | pearson_cosine | 0.8735 | |
| | **spearman_cosine** | **0.8192** | |
| |
| <!-- |
| ## Bias, Risks and Limitations |
| |
| *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
| --> |
| |
| <!-- |
| ### Recommendations |
| |
| *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| --> |
| |
| ## Training Details |
| |
| ### Training Dataset |
| |
| #### Unnamed Dataset |
| |
| * Size: 315 training samples |
| * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code> |
| * Approximate statistics based on the first 315 samples: |
| | | sentence1 | sentence2 | score | |
| |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------| |
| | type | string | string | float | |
| | details | <ul><li>min: 10 tokens</li><li>mean: 16.62 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 16.83 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.57</li><li>max: 1.0</li></ul> | |
| * Samples: |
| | sentence1 | sentence2 | score | |
| |:-------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------|:-----------------| |
| | <code>View button icon under Action column is not displayed properly</code> | <code>Unable to Search and Select Product in Select Product Catalog During In-House Replenishment</code> | <code>1.0</code> | |
| | <code>Pick Assignment Throws Replenishment Error Even When Primary Location Has Available Stock</code> | <code>Cycle Count Variance report not fetching latest cycle count data dynamically</code> | <code>0.8</code> | |
| | <code>Move Items UI should auto-hide location selection when only one Primary location exists</code> | <code>Primary Location not populated when product is fetched using Scan/Search Barcode in In-House Replenishment</code> | <code>0.8</code> | |
| * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: |
| ```json |
| { |
| "loss_fct": "torch.nn.modules.loss.MSELoss", |
| "cos_score_transformation": "torch.nn.modules.linear.Identity" |
| } |
| ``` |
| |
| ### Evaluation Dataset |
|
|
| #### Unnamed Dataset |
|
|
| * Size: 79 evaluation samples |
| * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code> |
| * Approximate statistics based on the first 79 samples: |
| | | sentence1 | sentence2 | score | |
| |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------| |
| | type | string | string | float | |
| | details | <ul><li>min: 10 tokens</li><li>mean: 16.51 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 17.03 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.63</li><li>max: 1.0</li></ul> | |
| * Samples: |
| | sentence1 | sentence2 | score | |
| |:--------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------|:-----------------| |
| | <code>Update Packing Slip date format to MM-DD-YYYY</code> | <code>Accounting Template data is not fetching under Template column in Sales History By Item report</code> | <code>0.8</code> | |
| | <code>Update Comments Section Format and Merge Herman ID / Employee ID Field</code> | <code>Order With Quantity Exceeding Available Primary Stock Is Marked Delivered Instead of Back Order and Creates Negative Stock</code> | <code>0.0</code> | |
| | <code>Default distribution center comment is not displayed in Comments section</code> | <code>Update Packing Slip date format to MM-DD-YYYY</code> | <code>0.8</code> | |
| * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: |
| ```json |
| { |
| "loss_fct": "torch.nn.modules.loss.MSELoss", |
| "cos_score_transformation": "torch.nn.modules.linear.Identity" |
| } |
| ``` |
|
|
| ### Training Hyperparameters |
| #### Non-Default Hyperparameters |
|
|
| - `per_device_train_batch_size`: 4 |
| - `learning_rate`: 2e-05 |
| - `warmup_steps`: 0.1 |
| - `fp16`: True |
| - `per_device_eval_batch_size`: 4 |
|
|
| #### All Hyperparameters |
| <details><summary>Click to expand</summary> |
|
|
| - `per_device_train_batch_size`: 4 |
| - `num_train_epochs`: 3 |
| - `max_steps`: -1 |
| - `learning_rate`: 2e-05 |
| - `lr_scheduler_type`: linear |
| - `lr_scheduler_kwargs`: None |
| - `warmup_steps`: 0.1 |
| - `optim`: adamw_torch_fused |
| - `optim_args`: None |
| - `weight_decay`: 0.0 |
| - `adam_beta1`: 0.9 |
| - `adam_beta2`: 0.999 |
| - `adam_epsilon`: 1e-08 |
| - `optim_target_modules`: None |
| - `gradient_accumulation_steps`: 1 |
| - `average_tokens_across_devices`: True |
| - `max_grad_norm`: 1.0 |
| - `label_smoothing_factor`: 0.0 |
| - `bf16`: False |
| - `fp16`: True |
| - `bf16_full_eval`: False |
| - `fp16_full_eval`: False |
| - `tf32`: None |
| - `gradient_checkpointing`: False |
| - `gradient_checkpointing_kwargs`: None |
| - `torch_compile`: False |
| - `torch_compile_backend`: None |
| - `torch_compile_mode`: None |
| - `use_liger_kernel`: False |
| - `liger_kernel_config`: None |
| - `use_cache`: False |
| - `neftune_noise_alpha`: None |
| - `torch_empty_cache_steps`: None |
| - `auto_find_batch_size`: False |
| - `log_on_each_node`: True |
| - `logging_nan_inf_filter`: True |
| - `include_num_input_tokens_seen`: no |
| - `log_level`: passive |
| - `log_level_replica`: warning |
| - `disable_tqdm`: False |
| - `project`: huggingface |
| - `trackio_space_id`: None |
| - `trackio_bucket_id`: None |
| - `trackio_static_space_id`: None |
| - `per_device_eval_batch_size`: 4 |
| - `prediction_loss_only`: True |
| - `eval_on_start`: False |
| - `eval_do_concat_batches`: True |
| - `eval_use_gather_object`: False |
| - `eval_accumulation_steps`: None |
| - `include_for_metrics`: [] |
| - `batch_eval_metrics`: False |
| - `save_only_model`: False |
| - `save_on_each_node`: False |
| - `enable_jit_checkpoint`: False |
| - `push_to_hub`: False |
| - `hub_private_repo`: None |
| - `hub_model_id`: None |
| - `hub_strategy`: every_save |
| - `hub_always_push`: False |
| - `hub_revision`: None |
| - `load_best_model_at_end`: False |
| - `ignore_data_skip`: False |
| - `restore_callback_states_from_checkpoint`: False |
| - `full_determinism`: False |
| - `seed`: 42 |
| - `data_seed`: None |
| - `use_cpu`: False |
| - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
| - `parallelism_config`: None |
| - `dataloader_drop_last`: False |
| - `dataloader_num_workers`: 0 |
| - `dataloader_pin_memory`: True |
| - `dataloader_persistent_workers`: False |
| - `dataloader_prefetch_factor`: None |
| - `remove_unused_columns`: True |
| - `label_names`: None |
| - `train_sampling_strategy`: random |
| - `length_column_name`: length |
| - `ddp_find_unused_parameters`: None |
| - `ddp_bucket_cap_mb`: None |
| - `ddp_broadcast_buffers`: False |
| - `ddp_static_graph`: None |
| - `ddp_backend`: None |
| - `ddp_timeout`: 1800 |
| - `fsdp`: [] |
| - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
| - `deepspeed`: None |
| - `debug`: [] |
| - `skip_memory_metrics`: True |
| - `do_predict`: False |
| - `resume_from_checkpoint`: None |
| - `warmup_ratio`: None |
| - `local_rank`: -1 |
| - `prompts`: None |
| - `batch_sampler`: batch_sampler |
| - `multi_dataset_batch_sampler`: proportional |
| - `router_mapping`: {} |
| - `learning_rate_mapping`: {} |
|
|
| </details> |
|
|
| ### Training Logs |
| | Epoch | Step | Training Loss | Validation Loss | ticket-similarity-eval_spearman_cosine | |
| |:------:|:----:|:-------------:|:---------------:|:--------------------------------------:| |
| | 0.0633 | 5 | 0.1581 | - | - | |
| | 0.1266 | 10 | 0.1581 | - | - | |
| | 0.1899 | 15 | 0.1117 | - | - | |
| | 0.2532 | 20 | 0.0869 | 0.0750 | 0.6907 | |
| | 0.3165 | 25 | 0.0651 | - | - | |
| | 0.3797 | 30 | 0.0590 | - | - | |
| | 0.4430 | 35 | 0.0580 | - | - | |
| | 0.5063 | 40 | 0.0698 | 0.1141 | 0.5602 | |
| | 0.5696 | 45 | 0.1079 | - | - | |
| | 0.6329 | 50 | 0.0932 | - | - | |
| | 0.6962 | 55 | 0.0762 | - | - | |
| | 0.7595 | 60 | 0.0938 | 0.0637 | 0.7089 | |
| | 0.8228 | 65 | 0.1259 | - | - | |
| | 0.8861 | 70 | 0.0735 | - | - | |
| | 0.9494 | 75 | 0.0276 | - | - | |
| | 1.0127 | 80 | 0.0551 | 0.0607 | 0.7692 | |
| | 1.0759 | 85 | 0.0788 | - | - | |
| | 1.1392 | 90 | 0.0807 | - | - | |
| | 1.2025 | 95 | 0.0334 | - | - | |
| | 1.2658 | 100 | 0.0508 | 0.0687 | 0.7471 | |
| | 1.3291 | 105 | 0.0719 | - | - | |
| | 1.3924 | 110 | 0.0404 | - | - | |
| | 1.4557 | 115 | 0.0143 | - | - | |
| | 1.5190 | 120 | 0.0740 | 0.0630 | 0.7372 | |
| | 1.5823 | 125 | 0.0410 | - | - | |
| | 1.6456 | 130 | 0.0483 | - | - | |
| | 1.7089 | 135 | 0.0629 | - | - | |
| | 1.7722 | 140 | 0.0513 | 0.0483 | 0.7610 | |
| | 1.8354 | 145 | 0.0175 | - | - | |
| | 1.8987 | 150 | 0.0397 | - | - | |
| | 1.9620 | 155 | 0.0341 | - | - | |
| | 2.0253 | 160 | 0.0223 | 0.0478 | 0.7755 | |
| | 2.0886 | 165 | 0.0167 | - | - | |
| | 2.1519 | 170 | 0.0230 | - | - | |
| | 2.2152 | 175 | 0.0600 | - | - | |
| | 2.2785 | 180 | 0.0357 | 0.0412 | 0.8031 | |
| | 2.3418 | 185 | 0.0479 | - | - | |
| | 2.4051 | 190 | 0.0172 | - | - | |
| | 2.4684 | 195 | 0.0183 | - | - | |
| | 2.5316 | 200 | 0.0213 | 0.0399 | 0.8162 | |
| | 2.5949 | 205 | 0.0115 | - | - | |
| | 2.6582 | 210 | 0.0305 | - | - | |
| | 2.7215 | 215 | 0.0101 | - | - | |
| | 2.7848 | 220 | 0.0189 | 0.0388 | 0.8229 | |
| | 2.8481 | 225 | 0.0249 | - | - | |
| | 2.9114 | 230 | 0.0104 | - | - | |
| | 2.9747 | 235 | 0.0099 | - | - | |
| | 3.0 | 237 | - | 0.0391 | 0.8192 | |
|
|
|
|
| ### Training Time |
| - **Training**: 46.3 minutes |
|
|
| ### Framework Versions |
| - Python: 3.12.13 |
| - Sentence Transformers: 5.4.1 |
| - Transformers: 5.7.0 |
| - PyTorch: 2.10.0+cu128 |
| - Accelerate: 1.13.0 |
| - Datasets: 4.8.5 |
| - Tokenizers: 0.22.2 |
|
|
| ## Citation |
|
|
| ### BibTeX |
|
|
| #### Sentence Transformers |
| ```bibtex |
| @inproceedings{reimers-2019-sentence-bert, |
| title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
| author = "Reimers, Nils and Gurevych, Iryna", |
| booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
| month = "11", |
| year = "2019", |
| publisher = "Association for Computational Linguistics", |
| url = "https://arxiv.org/abs/1908.10084", |
| } |
| ``` |
|
|
| <!-- |
| ## Glossary |
|
|
| *Clearly define terms in order to be accessible across audiences.* |
| --> |
|
|
| <!-- |
| ## Model Card Authors |
|
|
| *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
| --> |
|
|
| <!-- |
| ## Model Card Contact |
|
|
| *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
| --> |
